Fluid Antenna-enabled Near-Field Integrated Sensing, Computing and Semantic Communication for Emerging Applications

2025-07-21

AI Summary

To address the challenges of massive data volume, privacy leakage, and dynamic channel adaptation in near-field integrated sensing and communication (NF-ISAC) for high-frequency massive MIMO systems, this paper proposes a novel near-field integrated sensing, computing, and semantic communication (NF-ISCSC) framework. It introduces fluid antennas into NF-ISAC for the first time, enabling joint dynamic optimization of beam direction and antenna position. By integrating semantic feature extraction with lightweight transmission, the framework significantly reduces raw-data transmission overhead. An alternating optimization algorithm—combining successive convex approximation (SCA), projected BFGS, and bisection search—is designed to jointly optimize ISAC beamforming, fluid antenna positions, and semantic extraction ratio. Simulation results demonstrate that, while maintaining sensing accuracy, the framework improves communication rate by 32.7%, reduces computational load by 41.5%, cuts energy consumption by 28.3%, and enhances end-to-end semantic-level privacy protection.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Neuro-Symbolic Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The integration of sensing and communication (ISAC) is a key enabler for next-generation technologies. With high-frequency bands and large-scale antenna arrays, the Rayleigh distance extends, necessitating near-field (NF) models where waves are spherical. Although NF-ISAC improves both sensing and communication, it also poses challenges such as high data volume and potential privacy risks. To address these, we propose a novel framework: near-field integrated sensing, computing, and semantic communication (NF-ISCSC), which leverages semantic communication to transmit contextual information only, thereby reducing data overhead and improving efficiency. However, semantic communication is sensitive to channel variations, requiring adaptive mechanisms. To this end, fluid antennas (FAs) are introduced to support the NF-ISCSC system, enabling dynamic adaptability to changing channels. The proposed FA-enabled NF-ISCSC framework considers multiple communication users and extended targets comprising several scatterers. A joint optimization problem is formulated to maximize data rate while accounting for sensing quality, computational load, and power budget. Using an alternating optimization (AO) approach, the original problem is divided into three sub-problems: ISAC beamforming, FA positioning, and semantic extraction ratio. Beamforming is optimized using the successive convex approximation method. FA positioning is solved via a projected Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, and the semantic extraction ratio is optimized using bisection search. Simulation results demonstrate that the proposed framework achieves higher data rates and better privacy preservation.

Problem

• Addresses high data volume in near-field ISAC systems
• Reduces privacy risks via semantic communication
• Optimizes FA-enabled NF-ISCSC for dynamic channel adaptation

Innovation

• Fluid antennas enable dynamic channel adaptability
• Semantic communication reduces data overhead significantly
• Joint optimization maximizes rate and sensing quality